Measurement System Analysis (MSA) plays a crucial role in any quality management system. It ensures that the data collected from measurement systems is accurate, consistent, and reliable. But even when companies invest time and resources into conducting an MSA, the entire effort can fall short if the acceptance criteria are poorly defined.
At The ROI Alliance, we often see manufacturers and quality control teams struggle with the same missteps when setting acceptance thresholds. Whether you’re launching a new MSA initiative or reviewing your current practices, avoiding these five common mistakes can significantly improve your results and reduce costly variability in your processes. If you want to dive even deeper into MSA for your business, check out our MSA online course.
1. Using Arbitrary or Industry-Generic Standards
One of the most frequent mistakes in defining acceptance criteria for MSA is adopting one-size-fits-all standards without context. Terms like "10% R&R" (Gage Repeatability and Reproducibility) is “excellent” or 30% R&R “needs improvement” might sound familiar, but are they actually relevant to your specific process?
Let’s say you have two equally critical dimensions. One has a %R&R of 10% and the other a %R&R of 40%. Does that mean that the second gauge is not acceptable?
It does not.
%R&R is a measure of gauge capability, not of acceptability. Capability is only the measurement error compared to the spec width. If the first gauge measures a dimension where the process is highly variable and could be made outside of specification, your chance of calling good product bad or bad product good is much higher than if the second gauge measures a process that hardly ever varies off of target. That might mean that your 10% R&R gauge might not be acceptable and your 40% one might be acceptable.
Applying generic benchmarks might provide a false sense of security. Each measurement system is tied to a unique process, and the acceptable level of gauge variation should reflect its chance to misclassify what it is supposed to be measuring.
Solution: Customize your acceptance criteria based on the chance of misclassifying good as bad or bad as good. Use risk assessments, capability studies, and product requirements to inform your thresholds rather than relying solely on industry defaults.
2. Failing to Collaborate Across Teams
Another common pitfall is allowing only the quality control department to define the acceptance criteria without input from engineering, manufacturing, or even suppliers. This siloed approach can result in unrealistic criteria, or worse, criteria that don't reflect all the requirements for the measurement system.
A lack of collaboration often leads to disconnects between theoretical expectations and the on-the-ground experience. This can cause buying measurement systems with unneeded precision or excessive repair and maintenance costs..
Solution: Make acceptance criteria a cross-functional discussion. Engage stakeholders early in the MSA planning process. The best acceptance metrics often come from those closest to the process—operators, engineers, and quality control managers working together.
3. Overlooking Measurement System Capability Metrics
Many teams define acceptance criteria without a full understanding of what the measurement system is actually capable of. They may rely too heavily on %R&R alone and ignore other indicators like bias, linearity, or stability. This narrow focus can hide important issues.
For example, a measurement system might have acceptable % R&R but exhibit significant bias over time, especially as parts wear or conditions fluctuate.
Solution: Incorporate all key MSA metrics, such as bias, linearity, and stability, into your acceptance criteria. A measurement system's suitability must be evaluated holistically to ensure it meets the needs of all stakeholders.
4. Not Tying Criteria to Business Impact
There is no statistical calculation for acceptance criteria.. They should connect directly to your business goals, such as reducing scrap, improving customer satisfaction, or enabling predictive maintenance. Unfortunately, many companies set criteria without linking them to tangible outcomes.
This misalignment can result in either overly strict requirements that burden operations or overly loose ones that allow defects to slip through undetected.
Solution: Define acceptance criteria based on how measurement variation affects your business. If measurement error causes a $50,000 recall or leads to 5% rework, your thresholds should reflect that risk. Data-driven justification for acceptance criteria makes it easier to defend decisions and gain stakeholder support.
5. Neglecting to Revisit and Update MSA Acceptance Criteria
MSA is not a one-time event. As processes evolve, materials change, or equipment gets replaced, the relevance of your original acceptance criteria may degrade. Unfortunately, many teams treat these thresholds as static and fail to revisit them over time.
This can lead to outdated criteria that no longer reflect the current state of your measurement system or production needs.
Solution: Schedule regular reviews of your MSA acceptance criteria, especially after process changes, equipment upgrades, or major nonconformances. Make updates and longitudinal gauge studies a documented part of your quality system to ensure continuous improvement and relevance.
Why Defining MSA Acceptance Criteria Matters
Getting your MSA acceptance criteria right is about more than passing a study, it’s about building confidence in your data. Poorly defined thresholds lead to unreliable measurements, inconsistent quality control standards, and increased operational risk. But when you take a thoughtful, data-driven approach, you create a foundation for better decisions, improved efficiency, and lower cost of quality control.
At The ROI Alliance, we help organizations uncover inefficiencies in their measurement systems and define meaningful, practical acceptance criteria that support long-term success. If you're ready to take a smarter approach to Measurement System Analysis, we’re here to guide you through every step.
Final Thoughts
The suitability of a measurement system is only as strong as the criteria you use to evaluate it. Avoiding these five common mistakes can help you build a measurement process that supports your quality control objectives rather than undermining them.
Instead of defaulting to generic metrics or outdated standards, take time to define acceptance criteria that reflect your actual risks, processes, and business goals. With the right approach, your MSA will no longer be a checkbox, it will be a strategic tool for operational excellence. To learn even more about MSA and creating acceptability criteria, enroll in our online MSA training!
